Computers and Electronics in Agriculture · 2025 · 33 citations · 65 references
AgriculturePrecision AgricultureSustainable AgricultureAgricultural DevelopmentAgricultural PolicyCrop ProductionAgricultural Policy AnalysisAgricultural Production SystemsAgricultural SustainabilityAgricultural SystemsAgricultural TechnologyAgricultural Productivity EffectsAi CapabilitiesClimate ResilienceEnvironmental Sustainability PolicyHuman-machine Interaction In AgriculturePrecision Agriculture MonitoringPrecision Agriculture SystemsSustainable Agriculture Policy
The increasing global population underscores the need for sustainable agri-food systems. In this context, Artificial Intelligence (AI) has emerged as a transformative tool, offering innovative solutions to enhance agricultural productivity, optimize resource utilization, and promote environmental sustainability. Despite its growing relevance, the multifaceted implications of AI in terms of environmental, economic, political, social, and ethical remain insufficiently examined in the literature. This review synthesizes existing research to explore the ethical dimensions, transformative potential, and policy trajectories associated with AI integration in agriculture. It highlights AI’s capacity to improve input–output efficiency, enable real-time monitoring of crops and livestock, conserve soil and water resources, and reduce post-harvest losses, thereby contributing to the attainment of the United Nations Sustainable Development Goals (UN SDGs). The core domains of AI applications, including precision agriculture, crop surveillance, and climate resilience, were examined alongside prevailing challenges, such as inadequate technological infrastructure, limited data accessibility, skill deficits, and persistent ethical concerns. Findings from this review advocates for the equitable, socially responsible, and sustainable deployment of AI in agriculture, underscoring the importance of stakeholder trust and societal acceptance. In conclusion, the study proposes policy frameworks aimed at mitigating associated risks and addressing emerging ethical and governance challenges, while optimizing the socio-economic and environmental benefits of AI in agri-food systems. Noteworthy to mention is that this work contributes to the evolving discourse on harnessing AI for resilient, inclusive, and sustainable agricultural development.
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